Plasma circular RNA panel to diagnose hepatitis B virus-related hepatocellular carcinoma: A large-scale, multicenter study
Plasma circular RNA panel to diagnose hepatitis B virus-related hepatocellular carcinoma: A large-scale, multicenter study
复制标题
血浆环状 RNA 组合诊断乙型肝炎病毒相关肝细胞癌：一项大规模、多中心研究
DOI：
10.1002/ijc.32647
复制
发表时间：
2019-09-14
影响因子：
6.4
通讯作者：
Zhou, Wei-ping
中科院分区：
文献类型：
--
作者：
Yu, Jian;Ding, Wen-bing;Zhou, Wei-ping
为了探讨血浆环状RNA（circRNA）是否可以诊断乙型肝炎病毒（HBV）相关肝细胞癌（HCC），使用微阵列和qPCR来鉴定与对照（包括健康对照、慢性乙型肝炎和HBV相关肝硬化）相比，HBV相关HCC患者血浆circRNA增加。使用训练集 (n = 313) 构建逻辑回归模型，然后使用另外两个独立集（分别为 n = 306 和 526）进行验证。受试者工作特征曲线下面积（AUC）用于评估诊断准确性。我们鉴定出含有三种 circRNA（hsa_circ_0000976、hsa_circ_0007750 和 hsa_circ_0139897）的血浆 circRNA panel (CircPanel)，可以检测 HCC。在所有三组中，CircPanel 在区分 HCC 个体和对照组方面均表现出比 AFP（甲胎蛋白）更高的准确度（AUC，0.863 [95% 置信区间，CI：0.819-0.907] 与 0.790 [0.738-0.842]，训练组中 p = 0.036；训练组中为 0.843 [0.796-0.890] 与对照组。验证集 1 中为 0.747 [0.691-0.804]，p = 0.011，验证集 2 中为 0.864 [0.830-0.898] 与 0.769 [0.728-0.810]，p < 0.001）。 CircPanel 在检测小 HCC（孤立性、
To explore whether plasma circular RNAs (circRNAs) can diagnose hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC), microarray and qPCR were used to identify plasma circRNAs that were increased in HBV-related HCC patients compared to controls (including healthy controls, chronic hepatitis B and HBV-related liver cirrhosis). A logistic regression model was constructed using a training set (n = 313) and then validated using another two independent sets (n = 306 and 526, respectively). Area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. We identified a plasma circRNA panel (CircPanel) containing three circRNAs (hsa_circ_0000976, hsa_circ_0007750 and hsa_circ_0139897) that could detect HCC. CircPanel showed a higher accuracy than AFP (alpha-fetoprotein) to distinguish individuals with HCC from controls in all three sets (AUC, 0.863 [95% confidence interval, CI: 0.819-0.907] vs. 0.790 [0.738-0.842], p = 0.036 in training set; 0.843 [0.796-0.890] vs. 0.747 [0.691-0.804], p = 0.011 in validation set 1 and 0.864 [0.830-0.898] vs. 0.769 [0.728-0.810], p < 0.001 in validation set 2). CircPanel also performed well in detecting Small-HCC (solitary,